Bibliographic record
Abstract
McClure, Nikki. How To Be A Cat. New York, NY: Abrams Appleseed, 2013. Print.In line with Abrams high-quality art books, How To Be A Cat features self-taught artist Nikki McClure’s intricate cutout artwork and a glimpse into the daily activities of felines. Following a simple black-on-white color scheme with baby-blue highlights, readers are offered sixteen one-word lessons on how to be a cat, such as how to stretch, pounce, wait and dream.Using images and single text statements on each page, How To Be A Cat tells the story of a kitten learning about its environment. It awakens, cleans itself, and then, being playful, gets into trouble by attacking its adult cat companion, learning both how to pounce and how to be brave. It then receives a lesson in hunting—the art of simply listening—before exploring, getting lost, and inevitability being found again. The story therefore comes full circle, starting with the safety and security of life at home to the adventures and fears of the outside world and back home again where one can feast and sleep.McClure’s cutout felines bear a variety of adorable facial expressions, allowing readers the opportunity to connect and fall in love with this sweet little kitten. Further, the vignettes playfully convey feline behavior and profoundly depict the relationship of a child and its mentor, encompassing the associated trials and moments of tenderness.How To Be A Cat is recommended for elementary school libraries as well as public libraries, and would make a great book for story time. For, it is perfect for young readers, who, like the kitten in the story, are learning about themselves and the world around them.Highly recommended: 4 out of 4 starsReviewer: Jessica ThorlaksonJessica Thorlakson is a Public Services Librarian at the University of Alberta’s H. T. Coutts Education and Physical Education Library. She has a background in English Literature and enjoys little more than reading a good book and drinking some tea.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.040 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".